Your First AI application¶
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.
In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using this dataset from Oxford of 102 flower categories, you can see a few examples below.
The project is broken down into multiple steps:
- Load the image dataset and create a pipeline.
- Build and Train an image classifier on this dataset.
- Use your trained model to perform inference on flower images.
We'll lead you through each part which you'll implement in Python.
When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.
Import Resources¶
In [1]:
# The new version of dataset is only available in the tfds-nightly package.
%pip --no-cache-dir install tfds-nightly --user
# DON'T MISS TO RESTART THE KERNEL
Collecting tfds-nightly
Downloading https://files.pythonhosted.org/packages/b9/75/cf7790c6ebc4a89644ac38a136abf2ef33030520ed38eb281127c95b46b4/tfds_nightly-3.1.0.dev202007010106-py3-none-any.whl (3.4MB)
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Note: you may need to restart the kernel to use updated packages.
In [1]:
import warnings
warnings.filterwarnings('ignore')
In [2]:
# Import TensorFlow
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_hub as hub
In [3]:
# TODO: Make all other necessary imports.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import time
import matplotlib.pyplot as plt
import numpy as np
import json
import os
tfds.disable_progress_bar()
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
Load the Dataset¶
Here you'll use tensorflow_datasets to load the Oxford Flowers 102 dataset. This dataset has 3 splits: 'train', 'test', and 'validation'. You'll also need to make sure the training data is normalized and resized to 224x224 pixels as required by the pre-trained networks.
The validation and testing sets are used to measure the model's performance on data it hasn't seen yet, but you'll still need to normalize and resize the images to the appropriate size.
In [4]:
# Download data to default local directory "~/tensorflow_datasets"
!python -m tensorflow_datasets.scripts.download_and_prepare --register_checksums=True --datasets=oxford_flowers102
# TODO: Load the dataset with TensorFlow Datasets. Hint: use tfds.load()
dataset, dataset_info = tfds.load('oxford_flowers102', as_supervised = True, with_info = True)
I0702 04:45:08.298906 140437702616832 download_and_prepare.py:201] Running download_and_prepare for dataset(s):
oxford_flowers102
2020-07-02 04:45:08.351543: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with "Not found: Could not locate the credentials file.". Retrieving token from GCE failed with "Not found: Error executing an HTTP request: HTTP response code 404 with body '{"error":"invalid_request","error_description":"Service account not enabled on this instance"}'".
I0702 04:45:08.484077 140437702616832 dataset_info.py:427] Load pre-computed DatasetInfo (eg: splits, num examples,...) from GCS: oxford_flowers102/2.1.1
I0702 04:45:08.812161 140437702616832 dataset_info.py:358] Load dataset info from /tmp/tmpadpw25gctfds
I0702 04:45:08.823554 140437702616832 download_and_prepare.py:139] download_and_prepare for dataset oxford_flowers102/2.1.1...
I0702 04:45:08.824040 140437702616832 dataset_builder.py:346] Generating dataset oxford_flowers102 (/root/tensorflow_datasets/oxford_flowers102/2.1.1)
Downloading and preparing dataset oxford_flowers102/2.1.1 (download: 328.90 MiB, generated: 331.34 MiB, total: 660.25 MiB) to /root/tensorflow_datasets/oxford_flowers102/2.1.1...
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Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 17%|████▊ | 57/328 [00:03<00:10, 25.00 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 18%|████▉ | 58/328 [00:03<00:10, 25.00 MiB/s]
Extraction completed...: 0 file [00:03, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 18%|█████ | 59/328 [00:03<00:09, 27.01 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 18%|█████ | 60/328 [00:03<00:09, 27.01 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 19%|█████▏ | 61/328 [00:03<00:09, 27.01 MiB/s]
Extraction completed...: 0 file [00:03, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 19%|█████▎ | 62/328 [00:03<00:09, 27.45 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 19%|█████▍ | 63/328 [00:03<00:09, 27.45 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 20%|█████▍ | 64/328 [00:03<00:09, 27.45 MiB/s]
Extraction completed...: 0 file [00:03, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 20%|█████▌ | 65/328 [00:03<00:09, 27.81 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 20%|█████▋ | 66/328 [00:03<00:09, 27.81 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 20%|█████▋ | 67/328 [00:03<00:09, 27.81 MiB/s]
Extraction completed...: 0 file [00:03, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 21%|█████▊ | 68/328 [00:03<00:09, 28.06 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 21%|█████▉ | 69/328 [00:03<00:09, 28.06 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 21%|█████▉ | 70/328 [00:03<00:09, 28.06 MiB/s]
Extraction completed...: 0 file [00:03, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:03<00:00, 1.80 url/s]
Dl Size...: 22%|██████ | 71/328 [00:03<00:09, 28.30 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 22%|██████▏ | 72/328 [00:04<00:09, 28.30 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 22%|██████▏ | 73/328 [00:04<00:09, 28.30 MiB/s]
Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 23%|██████▎ | 74/328 [00:04<00:08, 28.37 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 23%|██████▍ | 75/328 [00:04<00:08, 28.37 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 23%|██████▍ | 76/328 [00:04<00:08, 28.37 MiB/s]
Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 23%|██████▌ | 77/328 [00:04<00:09, 27.66 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 24%|██████▋ | 78/328 [00:04<00:09, 27.66 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 24%|██████▋ | 79/328 [00:04<00:09, 27.66 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 24%|██████▊ | 80/328 [00:04<00:08, 27.66 MiB/s]
Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 25%|██████▉ | 81/328 [00:04<00:09, 27.07 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 25%|███████ | 82/328 [00:04<00:09, 27.07 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 25%|███████ | 83/328 [00:04<00:09, 27.07 MiB/s]
Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 26%|███████▏ | 84/328 [00:04<00:08, 27.56 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 26%|███████▎ | 85/328 [00:04<00:08, 27.56 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 26%|███████▎ | 86/328 [00:04<00:08, 27.56 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 27%|███████▍ | 87/328 [00:04<00:08, 27.56 MiB/s]
Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 27%|███████▌ | 88/328 [00:04<00:08, 28.88 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 27%|███████▌ | 89/328 [00:04<00:08, 28.88 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 27%|███████▋ | 90/328 [00:04<00:08, 28.88 MiB/s]
Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 28%|███████▊ | 91/328 [00:04<00:08, 29.01 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 28%|███████▊ | 92/328 [00:04<00:08, 29.01 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 28%|███████▉ | 93/328 [00:04<00:08, 29.01 MiB/s]
Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 29%|████████ | 94/328 [00:04<00:08, 29.14 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 29%|████████ | 95/328 [00:04<00:07, 29.14 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 29%|████████▏ | 96/328 [00:04<00:07, 29.14 MiB/s]
Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 30%|████████▎ | 97/328 [00:04<00:07, 29.29 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 30%|████████▎ | 98/328 [00:04<00:07, 29.29 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:04<00:00, 1.80 url/s]
Dl Size...: 30%|████████▍ | 99/328 [00:04<00:07, 29.29 MiB/s]
Extraction completed...: 0 file [00:04, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 30%|████████▏ | 100/328 [00:05<00:07, 29.17 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 31%|████████▎ | 101/328 [00:05<00:07, 29.17 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 31%|████████▍ | 102/328 [00:05<00:07, 29.17 MiB/s]
Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 31%|████████▍ | 103/328 [00:05<00:07, 29.10 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 32%|████████▌ | 104/328 [00:05<00:07, 29.10 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 32%|████████▋ | 105/328 [00:05<00:07, 29.10 MiB/s]
Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 32%|████████▋ | 106/328 [00:05<00:07, 28.66 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 33%|████████▊ | 107/328 [00:05<00:07, 28.66 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 33%|████████▉ | 108/328 [00:05<00:07, 28.66 MiB/s]
Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 33%|████████▉ | 109/328 [00:05<00:07, 27.75 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 34%|█████████ | 110/328 [00:05<00:07, 27.75 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 34%|█████████▏ | 111/328 [00:05<00:07, 27.75 MiB/s]
Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 34%|█████████▏ | 112/328 [00:05<00:07, 28.25 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 34%|█████████▎ | 113/328 [00:05<00:07, 28.25 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 35%|█████████▍ | 114/328 [00:05<00:07, 28.25 MiB/s]
Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 35%|█████████▍ | 115/328 [00:05<00:07, 28.73 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 35%|█████████▌ | 116/328 [00:05<00:07, 28.73 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 36%|█████████▋ | 117/328 [00:05<00:07, 28.73 MiB/s]
Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 36%|█████████▋ | 118/328 [00:05<00:07, 28.96 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 36%|█████████▊ | 119/328 [00:05<00:07, 28.96 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 37%|█████████▉ | 120/328 [00:05<00:07, 28.96 MiB/s]
Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 37%|█████████▉ | 121/328 [00:05<00:07, 29.15 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 37%|██████████ | 122/328 [00:05<00:07, 29.15 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 38%|██████████▏ | 123/328 [00:05<00:07, 29.15 MiB/s]
Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 38%|██████████▏ | 124/328 [00:05<00:07, 26.42 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 38%|██████████▎ | 125/328 [00:05<00:07, 26.42 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 38%|██████████▎ | 126/328 [00:05<00:07, 26.42 MiB/s]
Extraction completed...: 0 file [00:05, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:05<00:00, 1.80 url/s]
Dl Size...: 39%|██████████▍ | 127/328 [00:05<00:07, 27.28 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 39%|██████████▌ | 128/328 [00:06<00:07, 27.28 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 39%|██████████▌ | 129/328 [00:06<00:07, 27.28 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 40%|██████████▋ | 130/328 [00:06<00:07, 27.28 MiB/s]
Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 40%|██████████▊ | 131/328 [00:06<00:06, 28.25 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 40%|██████████▊ | 132/328 [00:06<00:06, 28.25 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 41%|██████████▉ | 133/328 [00:06<00:06, 28.25 MiB/s]
Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 41%|███████████ | 134/328 [00:06<00:06, 28.15 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 41%|███████████ | 135/328 [00:06<00:06, 28.15 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 41%|███████████▏ | 136/328 [00:06<00:06, 28.15 MiB/s]
Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 42%|███████████▎ | 137/328 [00:06<00:06, 28.06 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 42%|███████████▎ | 138/328 [00:06<00:06, 28.06 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 42%|███████████▍ | 139/328 [00:06<00:06, 28.06 MiB/s]
Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 43%|███████████▌ | 140/328 [00:06<00:06, 28.44 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 43%|███████████▌ | 141/328 [00:06<00:06, 28.44 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 43%|███████████▋ | 142/328 [00:06<00:06, 28.44 MiB/s]
Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 44%|███████████▊ | 143/328 [00:06<00:06, 28.37 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 44%|███████████▊ | 144/328 [00:06<00:06, 28.37 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 44%|███████████▉ | 145/328 [00:06<00:06, 28.37 MiB/s]
Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 45%|████████████ | 146/328 [00:06<00:06, 28.19 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 45%|████████████ | 147/328 [00:06<00:06, 28.19 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 45%|████████████▏ | 148/328 [00:06<00:06, 28.19 MiB/s]
Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 45%|████████████▎ | 149/328 [00:06<00:06, 27.85 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 46%|████████████▎ | 150/328 [00:06<00:06, 27.85 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 46%|████████████▍ | 151/328 [00:06<00:06, 27.85 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 46%|████████████▌ | 152/328 [00:06<00:06, 27.85 MiB/s]
Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 47%|████████████▌ | 153/328 [00:06<00:06, 27.05 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 47%|████████████▋ | 154/328 [00:06<00:06, 27.05 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:06<00:00, 1.80 url/s]
Dl Size...: 47%|████████████▊ | 155/328 [00:06<00:06, 27.05 MiB/s]
Extraction completed...: 0 file [00:06, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 48%|████████████▊ | 156/328 [00:07<00:06, 27.57 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 48%|████████████▉ | 157/328 [00:07<00:06, 27.57 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 48%|█████████████ | 158/328 [00:07<00:06, 27.57 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 48%|█████████████ | 159/328 [00:07<00:06, 27.57 MiB/s]
Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 49%|█████████████▏ | 160/328 [00:07<00:05, 29.09 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 49%|█████████████▎ | 161/328 [00:07<00:05, 29.09 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 49%|█████████████▎ | 162/328 [00:07<00:05, 29.09 MiB/s]
Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 50%|█████████████▍ | 163/328 [00:07<00:05, 28.94 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 50%|█████████████▌ | 164/328 [00:07<00:05, 28.94 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 50%|█████████████▌ | 165/328 [00:07<00:05, 28.94 MiB/s]
Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 51%|█████████████▋ | 166/328 [00:07<00:05, 28.80 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 51%|█████████████▋ | 167/328 [00:07<00:05, 28.80 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 51%|█████████████▊ | 168/328 [00:07<00:05, 28.80 MiB/s]
Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 52%|█████████████▉ | 169/328 [00:07<00:05, 28.91 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 52%|█████████████▉ | 170/328 [00:07<00:05, 28.91 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 52%|██████████████ | 171/328 [00:07<00:05, 28.91 MiB/s]
Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 52%|██████████████▏ | 172/328 [00:07<00:05, 28.90 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 53%|██████████████▏ | 173/328 [00:07<00:05, 28.90 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 53%|██████████████▎ | 174/328 [00:07<00:05, 28.90 MiB/s]
Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 53%|██████████████▍ | 175/328 [00:07<00:05, 28.86 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 54%|██████████████▍ | 176/328 [00:07<00:05, 28.86 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 54%|██████████████▌ | 177/328 [00:07<00:05, 28.86 MiB/s]
Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 54%|██████████████▋ | 178/328 [00:07<00:05, 28.86 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 55%|██████████████▋ | 179/328 [00:07<00:05, 28.86 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 55%|██████████████▊ | 180/328 [00:07<00:05, 28.86 MiB/s]
Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 55%|██████████████▉ | 181/328 [00:07<00:05, 28.27 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 55%|██████████████▉ | 182/328 [00:07<00:05, 28.27 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 56%|███████████████ | 183/328 [00:07<00:05, 28.27 MiB/s]
Extraction completed...: 0 file [00:07, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:07<00:00, 1.80 url/s]
Dl Size...: 56%|███████████████▏ | 184/328 [00:07<00:05, 28.69 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 56%|███████████████▏ | 185/328 [00:08<00:04, 28.69 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 57%|███████████████▎ | 186/328 [00:08<00:04, 28.69 MiB/s]
Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 57%|███████████████▍ | 187/328 [00:08<00:04, 28.80 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 57%|███████████████▍ | 188/328 [00:08<00:04, 28.80 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 58%|███████████████▌ | 189/328 [00:08<00:04, 28.80 MiB/s]
Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 58%|███████████████▋ | 190/328 [00:08<00:04, 28.92 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 58%|███████████████▋ | 191/328 [00:08<00:04, 28.92 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 59%|███████████████▊ | 192/328 [00:08<00:04, 28.92 MiB/s]
Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 59%|███████████████▉ | 193/328 [00:08<00:05, 26.18 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 59%|███████████████▉ | 194/328 [00:08<00:05, 26.18 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 59%|████████████████ | 195/328 [00:08<00:05, 26.18 MiB/s]
Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 60%|████████████████▏ | 196/328 [00:08<00:04, 26.92 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 60%|████████████████▏ | 197/328 [00:08<00:04, 26.92 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 60%|████████████████▎ | 198/328 [00:08<00:04, 26.92 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 61%|████████████████▍ | 199/328 [00:08<00:04, 26.92 MiB/s]
Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 61%|████████████████▍ | 200/328 [00:08<00:04, 27.83 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 61%|████████████████▌ | 201/328 [00:08<00:04, 27.83 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 62%|████████████████▋ | 202/328 [00:08<00:04, 27.83 MiB/s]
Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 62%|████████████████▋ | 203/328 [00:08<00:04, 28.19 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 62%|████████████████▊ | 204/328 [00:08<00:04, 28.19 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 62%|████████████████▉ | 205/328 [00:08<00:04, 28.19 MiB/s]
Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 63%|████████████████▉ | 206/328 [00:08<00:04, 28.65 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 63%|█████████████████ | 207/328 [00:08<00:04, 28.65 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 63%|█████████████████ | 208/328 [00:08<00:04, 28.65 MiB/s]
Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 64%|█████████████████▏ | 209/328 [00:08<00:04, 27.96 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 64%|█████████████████▎ | 210/328 [00:08<00:04, 27.96 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 64%|█████████████████▎ | 211/328 [00:08<00:04, 27.96 MiB/s]
Extraction completed...: 0 file [00:08, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:08<00:00, 1.80 url/s]
Dl Size...: 65%|█████████████████▍ | 212/328 [00:08<00:04, 28.34 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 65%|█████████████████▌ | 213/328 [00:09<00:04, 28.34 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 65%|█████████████████▌ | 214/328 [00:09<00:04, 28.34 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 66%|█████████████████▋ | 215/328 [00:09<00:03, 28.34 MiB/s]
Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 66%|█████████████████▊ | 216/328 [00:09<00:03, 28.55 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 66%|█████████████████▊ | 217/328 [00:09<00:03, 28.55 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 66%|█████████████████▉ | 218/328 [00:09<00:03, 28.55 MiB/s]
Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 67%|██████████████████ | 219/328 [00:09<00:04, 26.91 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 67%|██████████████████ | 220/328 [00:09<00:04, 26.91 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 67%|██████████████████▏ | 221/328 [00:09<00:03, 26.91 MiB/s]
Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 68%|██████████████████▎ | 222/328 [00:09<00:03, 27.46 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 68%|██████████████████▎ | 223/328 [00:09<00:03, 27.46 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 68%|██████████████████▍ | 224/328 [00:09<00:03, 27.46 MiB/s]
Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 69%|██████████████████▌ | 225/328 [00:09<00:03, 27.70 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 69%|██████████████████▌ | 226/328 [00:09<00:03, 27.70 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 69%|██████████████████▋ | 227/328 [00:09<00:03, 27.70 MiB/s]
Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 70%|██████████████████▊ | 228/328 [00:09<00:03, 27.90 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 70%|██████████████████▊ | 229/328 [00:09<00:03, 27.90 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 70%|██████████████████▉ | 230/328 [00:09<00:03, 27.90 MiB/s]
Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 70%|███████████████████ | 231/328 [00:09<00:03, 28.26 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 71%|███████████████████ | 232/328 [00:09<00:03, 28.26 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 71%|███████████████████▏ | 233/328 [00:09<00:03, 28.26 MiB/s]
Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 71%|███████████████████▎ | 234/328 [00:09<00:03, 28.62 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 72%|███████████████████▎ | 235/328 [00:09<00:03, 28.62 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 72%|███████████████████▍ | 236/328 [00:09<00:03, 28.62 MiB/s]
Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 72%|███████████████████▌ | 237/328 [00:09<00:03, 28.75 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 73%|███████████████████▌ | 238/328 [00:09<00:03, 28.75 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 73%|███████████████████▋ | 239/328 [00:09<00:03, 28.75 MiB/s]
Extraction completed...: 0 file [00:09, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 73%|███████████████████▊ | 240/328 [00:09<00:03, 28.62 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:09<00:00, 1.80 url/s]
Dl Size...: 73%|███████████████████▊ | 241/328 [00:09<00:03, 28.62 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 74%|███████████████████▉ | 242/328 [00:10<00:03, 28.62 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 74%|████████████████████ | 243/328 [00:10<00:02, 28.65 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 74%|████████████████████ | 244/328 [00:10<00:02, 28.65 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 75%|████████████████████▏ | 245/328 [00:10<00:02, 28.65 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 75%|████████████████████▎ | 246/328 [00:10<00:02, 28.65 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 75%|████████████████████▎ | 247/328 [00:10<00:02, 28.65 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 76%|████████████████████▍ | 248/328 [00:10<00:02, 28.65 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 76%|████████████████████▍ | 249/328 [00:10<00:02, 28.59 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 76%|████████████████████▌ | 250/328 [00:10<00:02, 28.59 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 77%|████████████████████▋ | 251/328 [00:10<00:02, 28.59 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 77%|████████████████████▋ | 252/328 [00:10<00:02, 28.62 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 77%|████████████████████▊ | 253/328 [00:10<00:02, 28.62 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 77%|████████████████████▉ | 254/328 [00:10<00:02, 28.62 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 78%|████████████████████▉ | 255/328 [00:10<00:02, 28.76 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 78%|█████████████████████ | 256/328 [00:10<00:02, 28.76 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 78%|█████████████████████▏ | 257/328 [00:10<00:02, 28.76 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 79%|█████████████████████▏ | 258/328 [00:10<00:02, 28.75 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 79%|█████████████████████▎ | 259/328 [00:10<00:02, 28.75 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 79%|█████████████████████▍ | 260/328 [00:10<00:02, 28.75 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 80%|█████████████████████▍ | 261/328 [00:10<00:02, 27.84 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 80%|█████████████████████▌ | 262/328 [00:10<00:02, 27.84 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 80%|█████████████████████▋ | 263/328 [00:10<00:02, 27.84 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 80%|█████████████████████▋ | 264/328 [00:10<00:02, 28.30 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 81%|█████████████████████▊ | 265/328 [00:10<00:02, 28.30 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 81%|█████████████████████▉ | 266/328 [00:10<00:02, 28.30 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 81%|█████████████████████▉ | 267/328 [00:10<00:02, 28.57 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 82%|██████████████████████ | 268/328 [00:10<00:02, 28.57 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:10<00:00, 1.80 url/s]
Dl Size...: 82%|██████████████████████▏ | 269/328 [00:10<00:02, 28.57 MiB/s]
Extraction completed...: 0 file [00:10, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 82%|██████████████████████▏ | 270/328 [00:11<00:02, 28.72 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 83%|██████████████████████▎ | 271/328 [00:11<00:01, 28.72 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 83%|██████████████████████▍ | 272/328 [00:11<00:01, 28.72 MiB/s]
Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 83%|██████████████████████▍ | 273/328 [00:11<00:02, 25.84 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 84%|██████████████████████▌ | 274/328 [00:11<00:02, 25.84 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 84%|██████████████████████▋ | 275/328 [00:11<00:02, 25.84 MiB/s]
Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 84%|██████████████████████▋ | 276/328 [00:11<00:01, 26.82 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 84%|██████████████████████▊ | 277/328 [00:11<00:01, 26.82 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 85%|██████████████████████▉ | 278/328 [00:11<00:01, 26.82 MiB/s]
Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 85%|██████████████████████▉ | 279/328 [00:11<00:01, 27.27 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 85%|███████████████████████ | 280/328 [00:11<00:01, 27.27 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 86%|███████████████████████▏ | 281/328 [00:11<00:01, 27.27 MiB/s]
Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 86%|███████████████████████▏ | 282/328 [00:11<00:01, 27.98 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 86%|███████████████████████▎ | 283/328 [00:11<00:01, 27.98 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 87%|███████████████████████▍ | 284/328 [00:11<00:01, 27.98 MiB/s]
Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 87%|███████████████████████▍ | 285/328 [00:11<00:01, 28.06 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 87%|███████████████████████▌ | 286/328 [00:11<00:01, 28.06 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 88%|███████████████████████▋ | 287/328 [00:11<00:01, 28.06 MiB/s]
Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 88%|███████████████████████▋ | 288/328 [00:11<00:01, 28.18 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 88%|███████████████████████▊ | 289/328 [00:11<00:01, 28.18 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 88%|███████████████████████▊ | 290/328 [00:11<00:01, 28.18 MiB/s]
Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 89%|███████████████████████▉ | 291/328 [00:11<00:01, 28.49 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 89%|████████████████████████ | 292/328 [00:11<00:01, 28.49 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 89%|████████████████████████ | 293/328 [00:11<00:01, 28.49 MiB/s]
Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 90%|████████████████████████▏ | 294/328 [00:11<00:01, 28.51 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 90%|████████████████████████▎ | 295/328 [00:11<00:01, 28.51 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 90%|████████████████████████▎ | 296/328 [00:11<00:01, 28.51 MiB/s]
Extraction completed...: 0 file [00:11, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:11<00:00, 1.80 url/s]
Dl Size...: 91%|████████████████████████▍ | 297/328 [00:11<00:01, 28.47 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 91%|████████████████████████▌ | 298/328 [00:12<00:01, 28.47 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 91%|████████████████████████▌ | 299/328 [00:12<00:01, 28.47 MiB/s]
Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 91%|████████████████████████▋ | 300/328 [00:12<00:00, 28.66 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 92%|████████████████████████▊ | 301/328 [00:12<00:00, 28.66 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 92%|████████████████████████▊ | 302/328 [00:12<00:00, 28.66 MiB/s]
Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 92%|████████████████████████▉ | 303/328 [00:12<00:00, 28.74 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 93%|█████████████████████████ | 304/328 [00:12<00:00, 28.74 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 93%|█████████████████████████ | 305/328 [00:12<00:00, 28.74 MiB/s]
Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 93%|█████████████████████████▏ | 306/328 [00:12<00:00, 28.68 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 94%|█████████████████████████▎ | 307/328 [00:12<00:00, 28.68 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 94%|█████████████████████████▎ | 308/328 [00:12<00:00, 28.68 MiB/s]
Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 94%|█████████████████████████▍ | 309/328 [00:12<00:00, 28.75 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 95%|█████████████████████████▌ | 310/328 [00:12<00:00, 28.75 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 95%|█████████████████████████▌ | 311/328 [00:12<00:00, 28.75 MiB/s]
Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 95%|█████████████████████████▋ | 312/328 [00:12<00:00, 28.49 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 95%|█████████████████████████▊ | 313/328 [00:12<00:00, 28.49 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 96%|█████████████████████████▊ | 314/328 [00:12<00:00, 28.49 MiB/s]
Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 96%|█████████████████████████▉ | 315/328 [00:12<00:00, 28.66 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 96%|██████████████████████████ | 316/328 [00:12<00:00, 28.66 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 97%|██████████████████████████ | 317/328 [00:12<00:00, 28.66 MiB/s]
Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 97%|██████████████████████████▏| 318/328 [00:12<00:00, 28.91 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 97%|██████████████████████████▎| 319/328 [00:12<00:00, 28.91 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 98%|██████████████████████████▎| 320/328 [00:12<00:00, 28.91 MiB/s]
Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 98%|██████████████████████████▍| 321/328 [00:12<00:00, 28.97 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 98%|██████████████████████████▌| 322/328 [00:12<00:00, 28.97 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 98%|██████████████████████████▌| 323/328 [00:12<00:00, 28.97 MiB/s]
Extraction completed...: 0 file [00:12, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 99%|██████████████████████████▋| 324/328 [00:12<00:00, 29.02 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:12<00:00, 1.80 url/s]
Dl Size...: 99%|██████████████████████████▊| 325/328 [00:12<00:00, 29.02 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:13<00:00, 1.80 url/s]
Dl Size...: 99%|██████████████████████████▊| 326/328 [00:13<00:00, 29.02 MiB/s]
Extraction completed...: 0 file [00:13, ? file/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:13<00:00, 1.80 url/s]
Dl Size...: 100%|██████████████████████████▉| 327/328 [00:13<00:00, 26.58 MiB/s]
Dl Completed...: 67%|█████████████████▎ | 2/3 [00:13<00:00, 1.80 url/s]
Dl Size...: 100%|███████████████████████████| 328/328 [00:13<00:00, 26.58 MiB/s]
Dl Completed...: 100%|██████████████████████████| 3/3 [00:13<00:00, 4.02s/ url]
Dl Size...: 100%|███████████████████████████| 328/328 [00:13<00:00, 26.58 MiB/s]
Dl Completed...: 100%|██████████████████████████| 3/3 [00:13<00:00, 4.02s/ url]
Dl Size...: 100%|███████████████████████████| 328/328 [00:13<00:00, 26.58 MiB/s]
Extraction completed...: 0%| | 0/1 [00:13<?, ? file/s]
Dl Completed...: 100%|██████████████████████████| 3/3 [00:19<00:00, 4.02s/ url]
Dl Size...: 100%|███████████████████████████| 328/328 [00:19<00:00, 26.58 MiB/s]
Extraction completed...: 100%|█████████████████| 1/1 [00:19<00:00, 19.47s/ file]
Extraction completed...: 100%|█████████████████| 1/1 [00:19<00:00, 19.47s/ file]
Dl Size...: 100%|███████████████████████████| 328/328 [00:19<00:00, 16.84 MiB/s]
Dl Completed...: 100%|██████████████████████████| 3/3 [00:19<00:00, 6.49s/ url]
I0702 04:45:28.437169 140437702616832 dataset_builder.py:947] Generating split train
Shuffling and writing examples to /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteUKS85V/oxford_flowers102-train.tfrecord
0%| | 0/1020 [00:00<?, ? examples/s]I0702 04:45:29.064554 140437702616832 tfrecords_writer.py:230] Done writing /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteUKS85V/oxford_flowers102-train.tfrecord. Shard lengths: [1020]
I0702 04:45:29.066515 140437702616832 dataset_builder.py:947] Generating split test
Shuffling and writing examples to /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteUKS85V/oxford_flowers102-test.tfrecord
98%|████████████████████████████▎| 6011/6149 [00:00<00:00, 19914.04 examples/s]I0702 04:45:32.588409 140437702616832 tfrecords_writer.py:230] Done writing /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteUKS85V/oxford_flowers102-test.tfrecord. Shard lengths: [3074, 3075]
I0702 04:45:32.594455 140437702616832 dataset_builder.py:947] Generating split validation
Shuffling and writing examples to /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteUKS85V/oxford_flowers102-validation.tfrecord
0%| | 0/1020 [00:00<?, ? examples/s]I0702 04:45:33.143527 140437702616832 tfrecords_writer.py:230] Done writing /root/tensorflow_datasets/oxford_flowers102/2.1.1.incompleteUKS85V/oxford_flowers102-validation.tfrecord. Shard lengths: [1020]
I0702 04:45:33.145424 140437702616832 dataset_builder.py:401] Skipping computing stats for mode ComputeStatsMode.SKIP.
Dataset oxford_flowers102 downloaded and prepared to /root/tensorflow_datasets/oxford_flowers102/2.1.1. Subsequent calls will reuse this data.
name: "oxford_flowers102"
description: "The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly occurring\nin the United Kingdom. Each class consists of between 40 and 258 images. The images have\nlarge scale, pose and light variations. In addition, there are categories that have large\nvariations within the category and several very similar categories.\n\nThe dataset is divided into a training set, a validation set and a test set.\nThe training set and validation set each consist of 10 images per class (totalling 1020 images each).\nThe test set consists of the remaining 6149 images (minimum 20 per class)."
citation: "@InProceedings{Nilsback08,\n author = \"Nilsback, M-E. and Zisserman, A.\",\n title = \"Automated Flower Classification over a Large Number of Classes\",\n booktitle = \"Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing\",\n year = \"2008\",\n month = \"Dec\"\n}"
location {
urls: "https://www.robots.ox.ac.uk/~vgg/data/flowers/102/"
}
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shard_lengths: 3074
shard_lengths: 3075
num_bytes: 260784877
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splits {
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In [5]:
# TODO: Create a training set, a validation set and a test set.
list(dataset.keys())
training_set = dataset['train']
test_set = dataset['test']
validation_set = dataset['validation']
In [6]:
dataset_info
Out[6]:
tfds.core.DatasetInfo(
name='oxford_flowers102',
version=2.1.1,
description='The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly occurring
in the United Kingdom. Each class consists of between 40 and 258 images. The images have
large scale, pose and light variations. In addition, there are categories that have large
variations within the category and several very similar categories.
The dataset is divided into a training set, a validation set and a test set.
The training set and validation set each consist of 10 images per class (totalling 1020 images each).
The test set consists of the remaining 6149 images (minimum 20 per class).',
homepage='https://www.robots.ox.ac.uk/~vgg/data/flowers/102/',
features=FeaturesDict({
'file_name': Text(shape=(), dtype=tf.string),
'image': Image(shape=(None, None, 3), dtype=tf.uint8),
'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=102),
}),
total_num_examples=8189,
splits={
'test': 6149,
'train': 1020,
'validation': 1020,
},
supervised_keys=('image', 'label'),
citation="""@InProceedings{Nilsback08,
author = "Nilsback, M-E. and Zisserman, A.",
title = "Automated Flower Classification over a Large Number of Classes",
booktitle = "Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing",
year = "2008",
month = "Dec"
}""",
redistribution_info=,
)
Explore the Dataset¶
In [7]:
# TODO: Get the number of examples in each set from the dataset info.
num_training_examples = dataset_info.splits['train'].num_examples
num_test_examples = dataset_info.splits['test'].num_examples
num_validation_examples = dataset_info.splits['validation'].num_examples
print('the number of training examples are: {}'.format(num_training_examples))
print('the number of test examples are: {}'.format(num_test_examples))
print('the number of validation examples are: {}'.format(num_validation_examples))
# TODO: Get the number of classes in the dataset from the dataset info.
num_classes = dataset_info.features['label'].num_classes
print('the number of classes in the dataset are: {}'.format(num_classes))
the number of training examples are: 1020 the number of test examples are: 6149 the number of validation examples are: 1020 the number of classes in the dataset are: 102
In [8]:
# TODO: Print the shape and corresponding label of 3 images in the training set.
for image, label in training_set.take(3):
print('The shape of this image is:', image.shape)
print('The label of the image is:', label.numpy())
The shape of this image is: (500, 667, 3) The label of the image is: 72 The shape of this image is: (500, 666, 3) The label of the image is: 84 The shape of this image is: (670, 500, 3) The label of the image is: 70
In [9]:
# TODO: Plot 1 image from the training set.
for image, label in training_set.take(1):
image = image.numpy().squeeze()
label = label.numpy()
plt.imshow(image, cmap= plt.cm.binary)
plt.colorbar()
plt.title(label)
plt.show()
print('The label of this image is:', label)
# Set the title of the plot to the corresponding image label.
The label of this image is: 72
Label Mapping¶
You'll also need to load in a mapping from label to category name. You can find this in the file label_map.json. It's a JSON object which you can read in with the json module. This will give you a dictionary mapping the integer coded labels to the actual names of the flowers.
In [10]:
with open('label_map.json', 'r') as f:
class_names = json.load(f)
class_names
Out[10]:
{'21': 'fire lily',
'3': 'canterbury bells',
'45': 'bolero deep blue',
'1': 'pink primrose',
'34': 'mexican aster',
'27': 'prince of wales feathers',
'7': 'moon orchid',
'16': 'globe-flower',
'25': 'grape hyacinth',
'26': 'corn poppy',
'79': 'toad lily',
'39': 'siam tulip',
'24': 'red ginger',
'67': 'spring crocus',
'35': 'alpine sea holly',
'32': 'garden phlox',
'10': 'globe thistle',
'6': 'tiger lily',
'93': 'ball moss',
'33': 'love in the mist',
'9': 'monkshood',
'102': 'blackberry lily',
'14': 'spear thistle',
'19': 'balloon flower',
'100': 'blanket flower',
'13': 'king protea',
'49': 'oxeye daisy',
'15': 'yellow iris',
'61': 'cautleya spicata',
'31': 'carnation',
'64': 'silverbush',
'68': 'bearded iris',
'63': 'black-eyed susan',
'69': 'windflower',
'62': 'japanese anemone',
'20': 'giant white arum lily',
'38': 'great masterwort',
'4': 'sweet pea',
'86': 'tree mallow',
'101': 'trumpet creeper',
'42': 'daffodil',
'22': 'pincushion flower',
'2': 'hard-leaved pocket orchid',
'54': 'sunflower',
'66': 'osteospermum',
'70': 'tree poppy',
'85': 'desert-rose',
'99': 'bromelia',
'87': 'magnolia',
'5': 'english marigold',
'92': 'bee balm',
'28': 'stemless gentian',
'97': 'mallow',
'57': 'gaura',
'40': 'lenten rose',
'47': 'marigold',
'59': 'orange dahlia',
'48': 'buttercup',
'55': 'pelargonium',
'36': 'ruby-lipped cattleya',
'91': 'hippeastrum',
'29': 'artichoke',
'71': 'gazania',
'90': 'canna lily',
'18': 'peruvian lily',
'98': 'mexican petunia',
'8': 'bird of paradise',
'30': 'sweet william',
'17': 'purple coneflower',
'52': 'wild pansy',
'84': 'columbine',
'12': "colt's foot",
'11': 'snapdragon',
'96': 'camellia',
'23': 'fritillary',
'50': 'common dandelion',
'44': 'poinsettia',
'53': 'primula',
'72': 'azalea',
'65': 'californian poppy',
'80': 'anthurium',
'76': 'morning glory',
'37': 'cape flower',
'56': 'bishop of llandaff',
'60': 'pink-yellow dahlia',
'82': 'clematis',
'58': 'geranium',
'75': 'thorn apple',
'41': 'barbeton daisy',
'95': 'bougainvillea',
'43': 'sword lily',
'83': 'hibiscus',
'78': 'lotus lotus',
'88': 'cyclamen',
'94': 'foxglove',
'81': 'frangipani',
'74': 'rose',
'89': 'watercress',
'73': 'water lily',
'46': 'wallflower',
'77': 'passion flower',
'51': 'petunia'}
In [11]:
for image, label in training_set.take(1):
image = image.numpy().squeeze()
label = str(label.numpy().squeeze())
print(class_names.get(label))
azalea
In [12]:
class_names_new = dict()
for key in class_names:
class_names_new[str(int(key)-1)] = class_names[key]
In [13]:
del class_names
In [14]:
class_names = class_names_new
In [15]:
class_names
Out[15]:
{'20': 'fire lily',
'2': 'canterbury bells',
'44': 'bolero deep blue',
'0': 'pink primrose',
'33': 'mexican aster',
'26': 'prince of wales feathers',
'6': 'moon orchid',
'15': 'globe-flower',
'24': 'grape hyacinth',
'25': 'corn poppy',
'78': 'toad lily',
'38': 'siam tulip',
'23': 'red ginger',
'66': 'spring crocus',
'34': 'alpine sea holly',
'31': 'garden phlox',
'9': 'globe thistle',
'5': 'tiger lily',
'92': 'ball moss',
'32': 'love in the mist',
'8': 'monkshood',
'101': 'blackberry lily',
'13': 'spear thistle',
'18': 'balloon flower',
'99': 'blanket flower',
'12': 'king protea',
'48': 'oxeye daisy',
'14': 'yellow iris',
'60': 'cautleya spicata',
'30': 'carnation',
'63': 'silverbush',
'67': 'bearded iris',
'62': 'black-eyed susan',
'68': 'windflower',
'61': 'japanese anemone',
'19': 'giant white arum lily',
'37': 'great masterwort',
'3': 'sweet pea',
'85': 'tree mallow',
'100': 'trumpet creeper',
'41': 'daffodil',
'21': 'pincushion flower',
'1': 'hard-leaved pocket orchid',
'53': 'sunflower',
'65': 'osteospermum',
'69': 'tree poppy',
'84': 'desert-rose',
'98': 'bromelia',
'86': 'magnolia',
'4': 'english marigold',
'91': 'bee balm',
'27': 'stemless gentian',
'96': 'mallow',
'56': 'gaura',
'39': 'lenten rose',
'46': 'marigold',
'58': 'orange dahlia',
'47': 'buttercup',
'54': 'pelargonium',
'35': 'ruby-lipped cattleya',
'90': 'hippeastrum',
'28': 'artichoke',
'70': 'gazania',
'89': 'canna lily',
'17': 'peruvian lily',
'97': 'mexican petunia',
'7': 'bird of paradise',
'29': 'sweet william',
'16': 'purple coneflower',
'51': 'wild pansy',
'83': 'columbine',
'11': "colt's foot",
'10': 'snapdragon',
'95': 'camellia',
'22': 'fritillary',
'49': 'common dandelion',
'43': 'poinsettia',
'52': 'primula',
'71': 'azalea',
'64': 'californian poppy',
'79': 'anthurium',
'75': 'morning glory',
'36': 'cape flower',
'55': 'bishop of llandaff',
'59': 'pink-yellow dahlia',
'81': 'clematis',
'57': 'geranium',
'74': 'thorn apple',
'40': 'barbeton daisy',
'94': 'bougainvillea',
'42': 'sword lily',
'82': 'hibiscus',
'77': 'lotus lotus',
'87': 'cyclamen',
'93': 'foxglove',
'80': 'frangipani',
'73': 'rose',
'88': 'watercress',
'72': 'water lily',
'45': 'wallflower',
'76': 'passion flower',
'50': 'petunia'}
In [16]:
# TODO: Plot 1 image from the training set. Set the title
# of the plot to the corresponding class name.
for image, label in training_set.take(1):
image = image.numpy().squeeze()
label = str(label.numpy().squeeze())
plt.imshow(image, cmap= plt.cm.binary)
plt.colorbar()
plt.title(class_names.get(label))
plt.show()
print(type(image))
print('The class name of this image is:', class_names.get(label))
<class 'numpy.ndarray'> The class name of this image is: water lily
Create Pipeline¶
In [17]:
# TODO: Create a pipeline for each set.
batch_size = 64
image_size = 224
def format_image(image, label):
image = tf.cast(image, tf.float32)
image = tf.image.resize(image, (image_size, image_size))
image /= 255
return image, label
training_batches = training_set.shuffle(num_training_examples//4).map(format_image).batch(batch_size).prefetch(1)
validation_batches = validation_set.map(format_image).batch(batch_size).prefetch(1)
testing_batches = test_set.map(format_image).batch(batch_size).prefetch(1)
Build and Train the Classifier¶
Now that the data is ready, it's time to build and train the classifier. You should use the MobileNet pre-trained model from TensorFlow Hub to get the image features. Build and train a new feed-forward classifier using those features.
We're going to leave this part up to you. If you want to talk through it with someone, chat with your fellow students!
Refer to the rubric for guidance on successfully completing this section. Things you'll need to do:
- Load the MobileNet pre-trained network from TensorFlow Hub.
- Define a new, untrained feed-forward network as a classifier.
- Train the classifier.
- Plot the loss and accuracy values achieved during training for the training and validation set.
- Save your trained model as a Keras model.
We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!
When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right.
Note for Workspace users: One important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module. Also, If your model is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with ls -lh), you should reduce the size of your hidden layers and train again.
In [18]:
# TODO: Build and train your network.
URL = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
feature_extractor = hub.KerasLayer(URL, input_shape=(image_size, image_size,3))
In [19]:
feature_extractor.trainable = False
In [20]:
model = tf.keras.Sequential([
feature_extractor,
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(num_classes, activation = 'softmax')
])
earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy',
min_delta=0.0001,
patience=1)
checkpoint_path = 'training_1/cp.ckpt'
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback=tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
save_best_only=True,
verbose=1)
model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= keras_layer (KerasLayer) (None, 1280) 2257984 _________________________________________________________________ dropout (Dropout) (None, 1280) 0 _________________________________________________________________ dense (Dense) (None, 102) 130662 ================================================================= Total params: 2,388,646 Trainable params: 130,662 Non-trainable params: 2,257,984 _________________________________________________________________
In [ ]:
In [21]:
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
EPOCHS = 100
history = model.fit(training_batches,
epochs=EPOCHS,
validation_data=validation_batches)
Epoch 1/100 16/16 [==============================] - 16s 1s/step - loss: 5.1161 - accuracy: 0.0206 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 2/100 16/16 [==============================] - 7s 463ms/step - loss: 3.7517 - accuracy: 0.1490 - val_loss: 3.0905 - val_accuracy: 0.4196 Epoch 3/100 16/16 [==============================] - 7s 414ms/step - loss: 2.7082 - accuracy: 0.3784 - val_loss: 2.4390 - val_accuracy: 0.5882 Epoch 4/100 16/16 [==============================] - 7s 441ms/step - loss: 2.0115 - accuracy: 0.5608 - val_loss: 2.0070 - val_accuracy: 0.6637 Epoch 5/100 16/16 [==============================] - 6s 400ms/step - loss: 1.5196 - accuracy: 0.7078 - val_loss: 1.7203 - val_accuracy: 0.7059 Epoch 6/100 16/16 [==============================] - 6s 402ms/step - loss: 1.2178 - accuracy: 0.7745 - val_loss: 1.5173 - val_accuracy: 0.7500 Epoch 7/100 16/16 [==============================] - 6s 397ms/step - loss: 0.9685 - accuracy: 0.8520 - val_loss: 1.3781 - val_accuracy: 0.7667 Epoch 8/100 16/16 [==============================] - 6s 399ms/step - loss: 0.8168 - accuracy: 0.8618 - val_loss: 1.2665 - val_accuracy: 0.7755 Epoch 9/100 16/16 [==============================] - 6s 397ms/step - loss: 0.6653 - accuracy: 0.9078 - val_loss: 1.1888 - val_accuracy: 0.7873 Epoch 10/100 16/16 [==============================] - 6s 397ms/step - loss: 0.5836 - accuracy: 0.9343 - val_loss: 1.1184 - val_accuracy: 0.7931 Epoch 11/100 16/16 [==============================] - 6s 395ms/step - loss: 0.4932 - accuracy: 0.9186 - val_loss: 1.0722 - val_accuracy: 0.7951 Epoch 12/100 16/16 [==============================] - 6s 396ms/step - loss: 0.4588 - accuracy: 0.9441 - val_loss: 1.0251 - val_accuracy: 0.8069 Epoch 13/100 16/16 [==============================] - 6s 403ms/step - loss: 0.3907 - accuracy: 0.9588 - val_loss: 0.9872 - val_accuracy: 0.8069 Epoch 14/100 16/16 [==============================] - 6s 403ms/step - loss: 0.3466 - accuracy: 0.9647 - val_loss: 0.9609 - val_accuracy: 0.7980 Epoch 15/100 16/16 [==============================] - 6s 402ms/step - loss: 0.3020 - accuracy: 0.9775 - val_loss: 0.9369 - val_accuracy: 0.8108 Epoch 16/100 16/16 [==============================] - 6s 403ms/step - loss: 0.2946 - accuracy: 0.9725 - val_loss: 0.9141 - val_accuracy: 0.8137 Epoch 17/100 16/16 [==============================] - 6s 401ms/step - loss: 0.2551 - accuracy: 0.9833 - val_loss: 0.8987 - val_accuracy: 0.8118 Epoch 18/100 16/16 [==============================] - 7s 412ms/step - loss: 0.2496 - accuracy: 0.9794 - val_loss: 0.8813 - val_accuracy: 0.8088 Epoch 19/100 16/16 [==============================] - 6s 400ms/step - loss: 0.2137 - accuracy: 0.9902 - val_loss: 0.8642 - val_accuracy: 0.8078 Epoch 20/100 16/16 [==============================] - 7s 409ms/step - loss: 0.1983 - accuracy: 0.9873 - val_loss: 0.8498 - val_accuracy: 0.8137 Epoch 21/100 16/16 [==============================] - 7s 406ms/step - loss: 0.1841 - accuracy: 0.9873 - val_loss: 0.8366 - val_accuracy: 0.8098 Epoch 22/100 16/16 [==============================] - 7s 411ms/step - loss: 0.1712 - accuracy: 0.9882 - val_loss: 0.8227 - val_accuracy: 0.8127 Epoch 23/100 16/16 [==============================] - 7s 415ms/step - loss: 0.1512 - accuracy: 0.9971 - val_loss: 0.8153 - val_accuracy: 0.8127 Epoch 24/100 16/16 [==============================] - 7s 419ms/step - loss: 0.1469 - accuracy: 0.9971 - val_loss: 0.8055 - val_accuracy: 0.8147 Epoch 25/100 16/16 [==============================] - 6s 404ms/step - loss: 0.1399 - accuracy: 0.9902 - val_loss: 0.8034 - val_accuracy: 0.8206 Epoch 26/100 16/16 [==============================] - 6s 400ms/step - loss: 0.1264 - accuracy: 0.9961 - val_loss: 0.7911 - val_accuracy: 0.8176 Epoch 27/100 16/16 [==============================] - 7s 412ms/step - loss: 0.1174 - accuracy: 0.9971 - val_loss: 0.7806 - val_accuracy: 0.8196 Epoch 28/100 16/16 [==============================] - 6s 401ms/step - loss: 0.1007 - accuracy: 0.9971 - val_loss: 0.7765 - val_accuracy: 0.8176 Epoch 29/100 16/16 [==============================] - 6s 405ms/step - loss: 0.1024 - accuracy: 0.9961 - val_loss: 0.7750 - val_accuracy: 0.8176 Epoch 30/100 16/16 [==============================] - 6s 405ms/step - loss: 0.0893 - accuracy: 1.0000 - val_loss: 0.7694 - val_accuracy: 0.8186 Epoch 31/100 16/16 [==============================] - 6s 405ms/step - loss: 0.0969 - accuracy: 0.9990 - val_loss: 0.7660 - val_accuracy: 0.8176 Epoch 32/100 16/16 [==============================] - 6s 406ms/step - loss: 0.0845 - accuracy: 0.9980 - val_loss: 0.7591 - val_accuracy: 0.8186 Epoch 33/100 16/16 [==============================] - 6s 401ms/step - loss: 0.0851 - accuracy: 0.9961 - val_loss: 0.7548 - val_accuracy: 0.8186 Epoch 34/100 16/16 [==============================] - 6s 398ms/step - loss: 0.0877 - accuracy: 0.9971 - val_loss: 0.7511 - val_accuracy: 0.8196 Epoch 35/100 16/16 [==============================] - 6s 404ms/step - loss: 0.0848 - accuracy: 0.9971 - val_loss: 0.7492 - val_accuracy: 0.8196 Epoch 36/100 16/16 [==============================] - 6s 397ms/step - loss: 0.0696 - accuracy: 0.9980 - val_loss: 0.7415 - val_accuracy: 0.8186 Epoch 37/100 16/16 [==============================] - 6s 392ms/step - loss: 0.0684 - accuracy: 1.0000 - val_loss: 0.7346 - val_accuracy: 0.8216 Epoch 38/100 16/16 [==============================] - 6s 398ms/step - loss: 0.0693 - accuracy: 0.9990 - val_loss: 0.7302 - val_accuracy: 0.8216 Epoch 39/100 16/16 [==============================] - 6s 406ms/step - loss: 0.0673 - accuracy: 0.9990 - val_loss: 0.7250 - val_accuracy: 0.8157 Epoch 40/100 16/16 [==============================] - 7s 409ms/step - loss: 0.0626 - accuracy: 0.9971 - val_loss: 0.7244 - val_accuracy: 0.8176 Epoch 41/100 16/16 [==============================] - 6s 406ms/step - loss: 0.0544 - accuracy: 1.0000 - val_loss: 0.7216 - val_accuracy: 0.8206 Epoch 42/100 16/16 [==============================] - 7s 410ms/step - loss: 0.0580 - accuracy: 1.0000 - val_loss: 0.7192 - val_accuracy: 0.8225 Epoch 43/100 16/16 [==============================] - 6s 399ms/step - loss: 0.0554 - accuracy: 0.9990 - val_loss: 0.7142 - val_accuracy: 0.8206 Epoch 44/100 16/16 [==============================] - 6s 401ms/step - loss: 0.0494 - accuracy: 1.0000 - val_loss: 0.7081 - val_accuracy: 0.8186 Epoch 45/100 16/16 [==============================] - 6s 404ms/step - loss: 0.0497 - accuracy: 0.9980 - val_loss: 0.7062 - val_accuracy: 0.8216 Epoch 46/100 16/16 [==============================] - 7s 409ms/step - loss: 0.0475 - accuracy: 0.9990 - val_loss: 0.7074 - val_accuracy: 0.8216 Epoch 47/100 16/16 [==============================] - 6s 396ms/step - loss: 0.0490 - accuracy: 0.9990 - val_loss: 0.7063 - val_accuracy: 0.8235 Epoch 48/100 16/16 [==============================] - 6s 389ms/step - loss: 0.0453 - accuracy: 1.0000 - val_loss: 0.7053 - val_accuracy: 0.8245 Epoch 49/100 16/16 [==============================] - 6s 392ms/step - loss: 0.0480 - accuracy: 0.9990 - val_loss: 0.6999 - val_accuracy: 0.8265 Epoch 50/100 16/16 [==============================] - 6s 393ms/step - loss: 0.0423 - accuracy: 1.0000 - val_loss: 0.6959 - val_accuracy: 0.8294 Epoch 51/100 16/16 [==============================] - 6s 402ms/step - loss: 0.0418 - accuracy: 0.9990 - val_loss: 0.6948 - val_accuracy: 0.8275 Epoch 52/100 16/16 [==============================] - 7s 426ms/step - loss: 0.0423 - accuracy: 0.9990 - val_loss: 0.6941 - val_accuracy: 0.8275 Epoch 53/100 16/16 [==============================] - 6s 401ms/step - loss: 0.0406 - accuracy: 0.9961 - val_loss: 0.6940 - val_accuracy: 0.8265 Epoch 54/100 16/16 [==============================] - 6s 394ms/step - loss: 0.0419 - accuracy: 0.9990 - val_loss: 0.6912 - val_accuracy: 0.8275 Epoch 55/100 16/16 [==============================] - 6s 394ms/step - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.6888 - val_accuracy: 0.8245 Epoch 56/100 16/16 [==============================] - 6s 406ms/step - loss: 0.0399 - accuracy: 1.0000 - val_loss: 0.6854 - val_accuracy: 0.8216 Epoch 57/100 16/16 [==============================] - 6s 402ms/step - loss: 0.0327 - accuracy: 1.0000 - val_loss: 0.6904 - val_accuracy: 0.8235 Epoch 58/100 16/16 [==============================] - 6s 399ms/step - loss: 0.0343 - accuracy: 1.0000 - val_loss: 0.6888 - val_accuracy: 0.8294 Epoch 59/100 16/16 [==============================] - 6s 397ms/step - loss: 0.0352 - accuracy: 0.9990 - val_loss: 0.6857 - val_accuracy: 0.8255 Epoch 60/100 16/16 [==============================] - 6s 405ms/step - loss: 0.0317 - accuracy: 1.0000 - val_loss: 0.6875 - val_accuracy: 0.8245 Epoch 61/100 16/16 [==============================] - 6s 392ms/step - loss: 0.0294 - accuracy: 1.0000 - val_loss: 0.6864 - val_accuracy: 0.8294 Epoch 62/100 16/16 [==============================] - 6s 399ms/step - loss: 0.0306 - accuracy: 1.0000 - val_loss: 0.6820 - val_accuracy: 0.8284 Epoch 63/100 16/16 [==============================] - 6s 400ms/step - loss: 0.0266 - accuracy: 1.0000 - val_loss: 0.6820 - val_accuracy: 0.8284 Epoch 64/100 16/16 [==============================] - 6s 394ms/step - loss: 0.0296 - accuracy: 1.0000 - val_loss: 0.6820 - val_accuracy: 0.8275 Epoch 65/100 16/16 [==============================] - 6s 393ms/step - loss: 0.0276 - accuracy: 1.0000 - val_loss: 0.6813 - val_accuracy: 0.8294 Epoch 66/100 16/16 [==============================] - 6s 398ms/step - loss: 0.0261 - accuracy: 1.0000 - val_loss: 0.6795 - val_accuracy: 0.8275 Epoch 67/100 16/16 [==============================] - 6s 397ms/step - loss: 0.0274 - accuracy: 1.0000 - val_loss: 0.6779 - val_accuracy: 0.8294 Epoch 68/100 16/16 [==============================] - 6s 403ms/step - loss: 0.0274 - accuracy: 1.0000 - val_loss: 0.6756 - val_accuracy: 0.8284 Epoch 69/100 16/16 [==============================] - 6s 400ms/step - loss: 0.0244 - accuracy: 1.0000 - val_loss: 0.6754 - val_accuracy: 0.8333 Epoch 70/100 16/16 [==============================] - 6s 395ms/step - loss: 0.0253 - accuracy: 0.9990 - val_loss: 0.6723 - val_accuracy: 0.8324 Epoch 71/100 16/16 [==============================] - 7s 418ms/step - loss: 0.0247 - accuracy: 0.9990 - val_loss: 0.6735 - val_accuracy: 0.8324 Epoch 72/100 16/16 [==============================] - 6s 399ms/step - loss: 0.0237 - accuracy: 1.0000 - val_loss: 0.6713 - val_accuracy: 0.8324 Epoch 73/100 16/16 [==============================] - 7s 412ms/step - loss: 0.0243 - accuracy: 1.0000 - val_loss: 0.6716 - val_accuracy: 0.8294 Epoch 74/100 16/16 [==============================] - 7s 415ms/step - loss: 0.0236 - accuracy: 1.0000 - val_loss: 0.6709 - val_accuracy: 0.8304 Epoch 75/100 16/16 [==============================] - 6s 404ms/step - loss: 0.0211 - accuracy: 1.0000 - val_loss: 0.6696 - val_accuracy: 0.8314 Epoch 76/100 16/16 [==============================] - 6s 395ms/step - loss: 0.0206 - accuracy: 1.0000 - val_loss: 0.6666 - val_accuracy: 0.8314 Epoch 77/100 16/16 [==============================] - 6s 397ms/step - loss: 0.0195 - accuracy: 1.0000 - val_loss: 0.6703 - val_accuracy: 0.8284 Epoch 78/100 16/16 [==============================] - 6s 392ms/step - loss: 0.0214 - accuracy: 1.0000 - val_loss: 0.6734 - val_accuracy: 0.8284 Epoch 79/100 16/16 [==============================] - 6s 395ms/step - loss: 0.0234 - accuracy: 1.0000 - val_loss: 0.6718 - val_accuracy: 0.8304 Epoch 80/100 16/16 [==============================] - 6s 394ms/step - loss: 0.0213 - accuracy: 0.9990 - val_loss: 0.6673 - val_accuracy: 0.8333 Epoch 81/100 16/16 [==============================] - 6s 392ms/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.6637 - val_accuracy: 0.8353 Epoch 82/100 16/16 [==============================] - 6s 402ms/step - loss: 0.0190 - accuracy: 0.9990 - val_loss: 0.6652 - val_accuracy: 0.8333 Epoch 83/100 16/16 [==============================] - 6s 404ms/step - loss: 0.0191 - accuracy: 1.0000 - val_loss: 0.6660 - val_accuracy: 0.8353 Epoch 84/100 16/16 [==============================] - 7s 408ms/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.6641 - val_accuracy: 0.8353 Epoch 85/100 16/16 [==============================] - 6s 406ms/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 0.6628 - val_accuracy: 0.8363 Epoch 86/100 16/16 [==============================] - 6s 402ms/step - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.6612 - val_accuracy: 0.8284 Epoch 87/100 16/16 [==============================] - 6s 401ms/step - loss: 0.0169 - accuracy: 0.9990 - val_loss: 0.6602 - val_accuracy: 0.8265 Epoch 88/100 16/16 [==============================] - 7s 407ms/step - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.6618 - val_accuracy: 0.8284 Epoch 89/100 16/16 [==============================] - 6s 401ms/step - loss: 0.0155 - accuracy: 1.0000 - val_loss: 0.6637 - val_accuracy: 0.8284 Epoch 90/100 16/16 [==============================] - 7s 429ms/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.6611 - val_accuracy: 0.8333 Epoch 91/100 16/16 [==============================] - 6s 403ms/step - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.6622 - val_accuracy: 0.8304 Epoch 92/100 16/16 [==============================] - 7s 435ms/step - loss: 0.0171 - accuracy: 0.9990 - val_loss: 0.6597 - val_accuracy: 0.8294 Epoch 93/100 16/16 [==============================] - 7s 429ms/step - loss: 0.0154 - accuracy: 1.0000 - val_loss: 0.6558 - val_accuracy: 0.8284 Epoch 94/100 16/16 [==============================] - 6s 399ms/step - loss: 0.0158 - accuracy: 0.9990 - val_loss: 0.6579 - val_accuracy: 0.8284 Epoch 95/100 16/16 [==============================] - 6s 400ms/step - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.6577 - val_accuracy: 0.8363 Epoch 96/100 16/16 [==============================] - 6s 403ms/step - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.6572 - val_accuracy: 0.8343 Epoch 97/100 16/16 [==============================] - 6s 400ms/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.6540 - val_accuracy: 0.8343 Epoch 98/100 16/16 [==============================] - 6s 399ms/step - loss: 0.0152 - accuracy: 1.0000 - val_loss: 0.6531 - val_accuracy: 0.8324 Epoch 99/100 16/16 [==============================] - 6s 402ms/step - loss: 0.0159 - accuracy: 1.0000 - val_loss: 0.6558 - val_accuracy: 0.8275 Epoch 100/100 16/16 [==============================] - 7s 434ms/step - loss: 0.0156 - accuracy: 0.9980 - val_loss: 0.6592 - val_accuracy: 0.8275
In [22]:
# TODO: Plot the loss and accuracy values achieved during
#training for the training and validation set.
training_accuracy = history.history['accuracy']
validation_accuracy = history.history['val_accuracy']
training_loss = history.history['loss']
validation_loss = history.history['val_loss']
epochs_range = range(EPOCHS)
plt.figure(figsize=(8, 8))
plt.subplot(1,2,1)
plt.plot(epochs_range, training_accuracy, label='Training Accuracy')
plt.plot(epochs_range, validation_accuracy, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1,2,2)
plt.plot(epochs_range, training_loss, label='Training loss')
plt.plot(epochs_range, validation_loss, label='Validation loss')
plt.legend(loc='upper right')
plt.title('Training and Validation loss')
plt.show()
Testing your Network¶
It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. You should be able to reach around 70% accuracy on the test set if the model has been trained well.
In [23]:
# TODO: Print the loss and accuracy values achieved on the entire test set.
model.evaluate(testing_batches)
97/97 [==============================] - 18s 186ms/step - loss: 0.7859 - accuracy: 0.7943
Out[23]:
[0.7859249970654851, 0.79427546]
Save the Model¶
Now that your network is trained, save the model so you can load it later for making inference. In the cell below save your model as a Keras model (i.e. save it as an HDF5 file).
In [24]:
# TODO: Save your trained model as a Keras model.
saved_keras_model_filepath = 'saved_model.h5'
model.save(saved_keras_model_filepath)
Load the Keras Model¶
Load the Keras model you saved above.
In [25]:
# TODO: Load the Keras model
#reloaded_SavedModel = tf.saved_model.load(savedModel_directory)
reloaded_keras_model = tf.keras.models.load_model(saved_keras_model_filepath, custom_objects={'KerasLayer':hub.KerasLayer})
reloaded_keras_model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= keras_layer (KerasLayer) (None, 1280) 2257984 _________________________________________________________________ dropout (Dropout) (None, 1280) 0 _________________________________________________________________ dense (Dense) (None, 102) 130662 ================================================================= Total params: 2,388,646 Trainable params: 130,662 Non-trainable params: 2,257,984 _________________________________________________________________
In [ ]:
In [ ]:
Inference for Classification¶
Now you'll write a function that uses your trained network for inference. Write a function called predict that takes an image, a model, and then returns the top $K$ most likely class labels along with the probabilities. The function call should look like:
probs, classes = predict(image_path, model, top_k)
If top_k=5 the output of the predict function should be something like this:
probs, classes = predict(image_path, model, 5)
print(probs)
print(classes)
> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]
> ['70', '3', '45', '62', '55']
Your predict function should use PIL to load the image from the given image_path. You can use the Image.open function to load the images. The Image.open() function returns an Image object. You can convert this Image object to a NumPy array by using the np.asarray() function.
The predict function will also need to handle pre-processing the input image such that it can be used by your model. We recommend you write a separate function called process_image that performs the pre-processing. You can then call the process_image function from the predict function.
Image Pre-processing¶
The process_image function should take in an image (in the form of a NumPy array) and return an image in the form of a NumPy array with shape (224, 224, 3).
First, you should convert your image into a TensorFlow Tensor and then resize it to the appropriate size using tf.image.resize.
Second, the pixel values of the input images are typically encoded as integers in the range 0-255, but the model expects the pixel values to be floats in the range 0-1. Therefore, you'll also need to normalize the pixel values.
Finally, convert your image back to a NumPy array using the .numpy() method.
In [26]:
# TODO: Create the process_image function
def process_image(image):
global dsize
image = tf.convert_to_tensor(image,tf.float32)
image = tf.image.resize(image,(image_size, image_size))
image/=255
return image
To check your process_image function we have provided 4 images in the ./test_images/ folder:
- cautleya_spicata.jpg
- hard-leaved_pocket_orchid.jpg
- orange_dahlia.jpg
- wild_pansy.jpg
The code below loads one of the above images using PIL and plots the original image alongside the image produced by your process_image function. If your process_image function works, the plotted image should be the correct size.
In [27]:
from PIL import Image
image_path = './test_images/orange_dahlia.jpg'
im = Image.open(image_path)
test_image = np.asarray(im)
processed_test_image = process_image(test_image)
fig, (ax1, ax2) = plt.subplots(figsize=(10,10), ncols=2)
ax1.imshow(test_image)
ax1.set_title('Original Image')
ax2.imshow(processed_test_image)
ax2.set_title('Processed Image')
plt.tight_layout()
plt.show()
Once you can get images in the correct format, it's time to write the predict function for making inference with your model.
Inference¶
Remember, the predict function should take an image, a model, and then returns the top $K$ most likely class labels along with the probabilities. The function call should look like:
probs, classes = predict(image_path, model, top_k)
If top_k=5 the output of the predict function should be something like this:
probs, classes = predict(image_path, model, 5)
print(probs)
print(classes)
> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]
> ['70', '3', '45', '62', '55']
Your predict function should use PIL to load the image from the given image_path. You can use the Image.open function to load the images. The Image.open() function returns an Image object. You can convert this Image object to a NumPy array by using the np.asarray() function.
Note: The image returned by the process_image function is a NumPy array with shape (224, 224, 3) but the model expects the input images to be of shape (1, 224, 224, 3). This extra dimension represents the batch size. We suggest you use the np.expand_dims() function to add the extra dimension.
In [28]:
# TODO: Create the predict function
def predict(image_path=None, model=None, top_k=None):
im = Image.open(image_path)
test_image = np.asarray(im)
processed_test_image = process_image(test_image)
print(image_path.split("/")[-1])
plt.imshow(processed_test_image)
processed_test_image=np.expand_dims(processed_test_image,0)
probs=model.predict(processed_test_image)
return tf.nn.top_k(probs, k=top_k)
Sanity Check¶
It's always good to check the predictions made by your model to make sure they are correct. To check your predictions we have provided 4 images in the ./test_images/ folder:
- cautleya_spicata.jpg
- hard-leaved_pocket_orchid.jpg
- orange_dahlia.jpg
- wild_pansy.jpg
In the cell below use matplotlib to plot the input image alongside the probabilities for the top 5 classes predicted by your model. Plot the probabilities as a bar graph. The plot should look like this:
You can convert from the class integer labels to actual flower names using class_names.
In [29]:
# TODO: Plot the input image along with the top 5 classes
def filtered(classes):
return [class_names.get(str(key)) if key else "Placeholder" for key in classes.numpy().squeeze().tolist()]
np.set_printoptions(formatter={'float_kind':'{:f}'.format})
In [37]:
filename='./test_images/cautleya_spicata.jpg'
probs, classes = predict(image_path=filename, model=model, top_k=5)
print("prediction probabilities :\n",probs)
print('prediction classes:\n',classes)
print(f"\u2022 File: {filename} \n\u2022 Probability: {probs[0]}\n\u2022 Classes: {classes}")
im = Image.open(filename)
test_image = np.asarray(im)
axis_label=filtered(classes)
fig,(ax1,ax2)=plt.subplots(figsize=(20,10),ncols=2)
ax1.imshow(test_image,cmap=plt.cm.binary)
ax2.set_title('Class Probability')
ax2.barh(np.array(axis_label),probs[0])
cautleya_spicata.jpg prediction probabilities : tf.Tensor([[0.965564 0.018569 0.010147 0.000901 0.000817]], shape=(1, 5), dtype=float32) prediction classes: tf.Tensor([[60 45 23 38 10]], shape=(1, 5), dtype=int32) • File: ./test_images/cautleya_spicata.jpg • Probability: [0.965564 0.018569 0.010147 0.000901 0.000817] • Classes: [[60 45 23 38 10]]
Out[37]:
<BarContainer object of 5 artists>
In [38]:
filename='./test_images/hard-leaved_pocket_orchid.jpg'
probs, classes = predict(image_path=filename, model=model, top_k=5)
print(f"\u2022 File: {filename} \n\u2022 Probability: {probs[0]}\n\u2022 Classes: {classes}")
im = Image.open(filename)
test_image = np.asarray(im)
axis_label=filtered(classes)
fig,(ax1,ax2)=plt.subplots(figsize=(20,10),ncols=2)
ax1.imshow(test_image,cmap=plt.cm.binary)
ax2.set_title('Class Probability')
ax2.barh(np.array(axis_label),probs[0])
hard-leaved_pocket_orchid.jpg • File: ./test_images/hard-leaved_pocket_orchid.jpg • Probability: [0.999779 0.000048 0.000047 0.000041 0.000032] • Classes: [[ 1 6 67 5 79]]
Out[38]:
<BarContainer object of 5 artists>
In [39]:
filename='./test_images/orange_dahlia.jpg'
probs, classes = predict(image_path=filename, model=model, top_k=5)
print(f"\u2022 File: {filename} \n\u2022 Probability: {probs[0]}\n\u2022 Classes: {classes}")
im = Image.open(filename)
test_image = np.asarray(im)
axis_label=filtered(classes)
fig,(ax1,ax2)=plt.subplots(figsize=(20,10),ncols=2)
ax1.imshow(test_image,cmap=plt.cm.binary)
ax2.set_title('Class Probability')
ax2.barh(np.array(axis_label),probs[0])
orange_dahlia.jpg • File: ./test_images/orange_dahlia.jpg • Probability: [0.514062 0.310469 0.051119 0.025070 0.023075] • Classes: [[58 4 99 65 40]]
Out[39]:
<BarContainer object of 5 artists>
In [40]:
filename='./test_images/wild_pansy.jpg'
probs, classes = predict(image_path=filename, model=model, top_k=5)
print(f"\u2022 File: {filename} \n\u2022 Probability: {probs[0]}\n\u2022 Classes: {classes}")
im = Image.open(filename)
test_image = np.asarray(im)
axis_label=filtered(classes)
fig,(ax1,ax2)=plt.subplots(figsize=(20,10),ncols=2)
ax1.imshow(test_image,cmap=plt.cm.binary)
ax2.set_title('Class Probability')
ax2.barh(np.array(axis_label),probs[0])
wild_pansy.jpg • File: ./test_images/wild_pansy.jpg • Probability: [0.999209 0.000300 0.000183 0.000053 0.000046] • Classes: [[51 63 18 33 81]]
Out[40]:
<BarContainer object of 5 artists>
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!!jupyter nbconvert *.ipynb
In [ ]: